Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements

Authors:
Vladimir M. Krasnopolsky;Helmut Schiller
Affiliations:
Science Applications International Corporation at National Centers for Environmental Prediction, 5200 Auth Road, NWS/NOAA, Camp Spring, MD;GKSS Research Center, Geesthacht, Germany
Venue:
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Year:
2003

Citing 6
Cited 8

On the approximate realization of continuous mappings by neural networks

Neural Networks
Approximation capabilities of multilayer feedforward networks

Neural Networks
Approximation of functions by a multilayer perceptron: a new approach

Neural Networks
Theoretical investigation of the robustness of multilayer perceptrons: analysis of the linear case and extension to nonlinear networks

IEEE Transactions on Neural Networks
Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks

IEEE Transactions on Neural Networks
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

IEEE Transactions on Neural Networks

Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental numerical models

Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
2006 Special issue: Computational intelligence in earth sciences and environmental applications: Issues and challenges

Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2006 Special issue: Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction

Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2006 Special issue: Applications of neural network methods to the processing of earth observation satellite data

Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
2007 Special Issue: Model inversion by parameter fit using NN emulating the forward model - Evaluation of indirect measurements

Neural Networks
2008 Special Issue: Neural network approach for robust and fast calculation of physical processes in numerical environmental models: Compound parameterization with a quality control of larger errors

Neural Networks
Solving inverse problems by decomposition, classification and simple modeling

Information Sciences: an International Journal
Semi-physical neural modeling for linear signal restoration

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

A broad class of neural network (NN) applications dealing with the remote measurements of geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and land surface is presented. In order to infer these parameters from remote sensing (RS) measurements, standard retrieval and variational techniques are applied. Both techniques require a data converter (transfer function or forward model) to convert satellite measurements into geophysical parameters or vice versa. In many cases, the transfer function and the forward model can be represented as a continuous nonlinear mapping. Because the NN technique is a generic technique for nonlinear mapping, it can be used beneficially for modeling transfer functions and forward models. These applications are introduced in a broader framework of solving forward and inverse problems in RS. In this broader context, we show that NN is an appropriate and efficient tool for solving forward and inverse problems in RS and for developing fast and accurate forward models and accurate and robust retrieval algorithms. Theoretical considerations are illustrated by several real-life examples--operational NN applications developed by the authors for SSM/I and medium resolution imaging spectrometer sensors.